Molecular Ecology (2002)

11

, 1717–1729

© 2002 Blackwell Science Ltd

Blackwell Science, Ltd

Late glacial history of the cold-adapted freshwater fish

Cottus gobio,

revealed by microsatellites

B . HÄNFLING,

*

B . HELLEMANS,

†

F . A . M. VOLCKAERT

†

and G. R . CARVALHO

*

*

Molecular Ecology and Fisheries Genetics Laboratory, Department of Biological Sciences, University of Hull, Hull HU6 7RX, UK,

†

Laboratory of Aquatic Ecology, Katholieke Universiteit Leuven, Ch. de Beriotstraat 32, B-3000 Leuven, Belgium

Abstract

The distribution of genetic diversity at 10 highly polymorphic microsatellite loci within theEuropean freshwater fish,

Cottus gobio

, L. was examined. The sampling range compriseda large geographical scale including lineages known to be highly divergent at both mito-chondrial DNA (mtDNA) and allozymes. An analysis of genetic variability within popula-tions showed that expected heterozygosity and allelic richness could be explained largelyby current effective population sizes. Evidence was found, however, that historical processespredating the last major glaciation affected allelic richness. In addition to confirming thelarge-scale patterns from earlier studies, the microsatellite data revealed new insights intorecent processes by analysing genetic structure within ancient lineages defined by mtDNAdata. Stepwise mutation model (SMM) and nonSMM-based methods demonstrated aclear genetic structuring within the Northwestern European lineage comprising popula-tions from Britain and Belgium, and within the Central European lineage populationsfrom the rivers Danube, Elbe and Main. Supported by an analysis of genetic variabilitywithin populations these results showed that the bullhead populations most probablypersisted throughout the last major glaciation within the British Isles and within the drain-ages of the rivers Elbe and Main. Such observations provide the first genetic evidence fora glacial refugium in such close proximity to the European glacial margins.

Keywords

:

Cottidae, Europe, phylogeography, Pleistocene, sculpin, northern refugia

Received 7 November 2001; revision received 25 April 2002; accepted 7 May 2002

Introduction

Knowledge on the global impact of Quaternary climate andgeography on the genetic structure of species and popula-tions has increased dramatically (Avise 2000; Hewitt 2000).Climatic fluctuations in addition to geographical and geo-logical modifications have affected the natural range andhabitat of most species markedly throughout the Pleistocene.During cold periods, most species shifted southwards intoso-called glacial refugia, whereas interglacials were followedby northerly range expansions (Hewitt 1996). The effectsof glacial events have left a genomic signature, resultingin concordant phylogeographical structures among manyspecies. For example, Hewitt (1999) identified three generalphylogeographical patterns for terrestrial animal and plantspecies in Europe. Furthermore, population genetic theory

predicts that processes associated with range shifts duringthe Pleistocene history of a species such as fragmentation,range expansion and reduced population size, will impactgenetic variability. A loss of genetic diversity during post-glacial population expansion has been modelled (Ibrahim

et al

. 1996), and has also been shown empirically, for examplein North American salmonid fishes (Bernatchez & Wilson1998).

Recently, an increasing number of papers have invest-igated the phylogeography of European primary fresh-water fishes using mitochondrial DNA (mtDNA) sequenceanalysis (Durand

et al

. 1999; Nesbø

et al

. 1999; Kotlik &Berrebi 2001) revealing in all cases distinct clades that dif-ferentiated hundreds of thousands of years ago. One suchcase is the bullhead,

Cottus gobio

, a small freshwater fishwidely distributed across Europe (Lelek 1987), for whichseven major clades of mtDNA haplotype lineage have beendescribed (Englbrecht

et al

. 2000; Volckaert

et al

. 2002)across Europe. Although the precise dating of clade origins

Correspondence: B. Hänfling. Fax: + 44 1482 465458; E-mail:b.haenfling@biosci.hull.ac.uk

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is tentative and the estimates vary among studies, bothEnglbrecht

et al

. (2000) and Volckaert

et al

. 2002) assumethat the divergence of all clades predates at least the lastmajor glaciation. The southern limit of some clades as farnorth as Belgium has shown clearly that in contrast toother European freshwater fish,

C. gobio

must havesurvived in the permafrost regions of Central Europethrough most major glaciations. Such a geographical distri-bution suggests the possible existence of a glacial refugiumwithin the vicinity of the North Sea (Englbrecht

et al

. 2000;Volckaert

et al

. 2002). Recent advances in the statisticalanalysis of DNA sequence data such as nested clade ana-lysis and mismatch distribution have been applied success-fully to analyse the determinants of genetic structure withinclearly defined haplotype lineages in some species (Nesbo

et al

. 1999; Bernatchez 2001). However, nested clade ana-lysis within haplotype lineages of the bullhead has resultedin many inconclusive outcomes (Volckaert

et al

. 2002). Fur-thermore, the ability of mtDNA to disclose processes on arecent time scale, such as around the last glacial maximum(LGM), is constrained by relatively low mutation rates.More rapidly evolving markers such as microsatelliteshave proved recently to be more informative on suchevolutionary time-scales (e.g. Angers & Bernatchez 1998).Indeed, the simultaneous employment of marker setsdiffering in their evolutionary dynamics is an especiallypowerful approach to disentangling major colonizationevents and subsequent, more recent population processes(Hewitt 2001).

The objective here is to examine events in the latePleistocene history of

C. gobio

such as range shifts andpalaeorefugia using a microsatellite analysis of populationstructure within previously predefined haplotype lineages.

Materials and methods

Characterization of populations

C. gobio

is a cold stenothermous freshwater fish, with acorresponding range within a river system often extendingclose to the source. Further downstream, higher temper-atures and lower oxygen levels limit its distribution.Accordingly,

C. gobio

in Central Europe is confined toheadwaters, occurring usually in distinct distributionalpatches. Because average individual dispersal distancesof bullheads are low (< 1 km, Downhower

et al

. 1990),two distributional patches can be regarded as distinctpopulations if they are separated by several kilometres ofbullhead-free river.

In the absence of empirical information on populationsize, the geographical range of populations can be used asa surrogate for population size (Frankham 1996). Since thespatial extent of distinct bullhead populations along a rivercourse is often well documented, such an approach was

used previously to estimate effective population size inCentral Europe (

patch size

, Hänfling & Brandl 1998b). How-ever, in cooler Northern Europe and alpine regions, thebullhead can extend into lowland rivers and lakes. In suchdownstream habitats it is more difficult to access popu-lations, and thus to estimate patch size. Alternatively, ameasure that can be applied to most populations was thelength of the river measured from a sample site to theupstream margin of the population. We call this measuresubsequently

upstream size

. Information on the geograph-ical extent of bullhead populations was obtained frompublished data (Schadt 1993) and from local fishery author-ities and Environment Agencies. For the population fromthe river Ulicka (V1) no such information was available.

Sample sites and populations

Bullheads from 44 distinct populations (

n

genes

= 32–122)were obtained from either existing collections (individualsanalysed in Hänfling & Brandl 1998a) or from new collec-tions representing five major European drainage basins(Danube, Rhine, Elbe, Maas and Scheldt), plus various smallerdrainage systems across the United Kingdom and NorthernEurope by electrofishing (see Table 1 and Fig. 1). Fin clipsor muscle tissue was preserved in either 100% ethanolor NaCl-saturated dimethylsulphoxide. DNA wasextracted using a salting-out method according to Bruford

et al

. (1992).The present study focused mainly on a shallow phylo-

genetic level using microsatellite analysis. However, tostrengthen the power of the analyses and to analyse theimpact of historical events on genetic variability, inde-pendent data sets on mtDNA were employed to assignpopulations into distinct phylogenetic groups. First,sequence information from a 482-bp fragment of the mito-chondrial control region (Englbrecht

et al

. 2000; Volckaert

et al

. 2002; Table 1) were used to assign all populations,with the exception of one, to one of the six phylogeograph-ical haplotype lineages unambiguously as described inEnglbrecht

et al

. (2000). One population of the river Scheldt(29) showed haplotypes of the Central European clade,which probably represents a rare example of introgressionby anthropogenic transfer of bullheads (Volckaert

et al

.2002). Thus, this population was excluded from the ana-lyses of genetic variability. Second, within the two majorhaplotype lineages (I and IV), populations were assignedto geographical groups (Table 1). Continental Europeanpopulations were grouped according to drainage systems(Danube, Elbe, Main and Scheldt) which proved to begenetically distinct by allozymes (Hänfling & Brandl1998a). Due to the small geographical extent of Britishdrainages, British bullhead populations were assigned intoa Northern (Northern England, Wales) and a Southerngroup (Cornwall, Hampshire).

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Analysis of microsatellite genotypes

Overall, 1328 individuals from 44 distinct populations weregenotyped at a set of 10 polymorphic microsatellite loci fromEnglbrecht

et al

. (1999) using an ALFexpress™ automaticsequencer (Pharmacia, Uppsala, Sweden). Polymerase chainreaction (PCR) conditions followed Englbrecht

et al

. (1999),

with slight modifications in annealing temperatures. Threeto four internal size standards were used for each locus inorder to score the size of the amplified fragments reliably.Additionally, the same two individuals were run on all gelsin order to facilitate cross-referencing among gels. Threeof these loci (Cgo1114PBBE, Cgo33ZIM and Cgo34ZIM)contain imperfect or complex microsatellite motifs and are

Table 1 Geographical location of sampling sites of Cottus gobio including drainage basin and geographical coordinates. The column labelledhaplotype lineage shows the assignment of each sample to one of the distinct haplotype lineages defined by Englbrecht et al. (2000). Thefollowing columns show sample size (number of genes, ngenes), genetic variability (observed average number of alleles per locus, ko; expectednumber of alleles at the mean sample size of 60 genes, k60 (Ewens 1972) and expected heterozygosity, He, unbiased estimate (Nei 1978) andpopulation size as approximated by patch size and upstream size (see text for explanation)

Ref. SiteHaplotype lineage

Geographical group Longitude Latitude ngenes kn k60 HE

Patch size

Upstream size

1 Ulicka I Danube 22°27′ E 48°57′ N 46 7.6 8.44 0.582 Turiec I Danube 18°51′ E 48°54′ N 50 7.9 8.51 0.68 40 353 Drietomica I Danube 17°49′ E 48°53′ N 40 4.3 4.78 0.47 5 44 Otterbach I Danube 12°15′ E 49°01′ N 37.8 4 4.53 0.44 16 55 Weisser Regen I Danube 12°52′ E 49°10′ N 38 7.1 8.51 0.63 45 406 Fichtelnaab I Danube 11°53′ E 49°58′ N 33.8 2.9 3.33 0.34 3 27 Loisach I Danube 11°10′ E 47°36′ N 37.4 6.9 8.38 0.63 558 Mindel I Danube 10°27′ E 48°07′ N 48 6.3 6.82 0.58 120 709 Oedweissbach I Elbe 12°05′ E 49°58′ N 36 3.6 4.06 0.50 5 410 Steinselb I Elbe 12°04′ E 50°08′ N 34 2.1 2.29 0.29 3 211 Suedl. Regnitz I Elbe 12°03′ E 50°18′ N 44 3.5 3.74 0.42 11 713 Enziusbach I Elbe 11°44′ E 50°12′ N 36 2.8 3.07 0.38 4 314 Kullean I Elbe 13°52′ E 55°31′ N 32 3.5 3.38 0.42 2015 Thiemitz I Rhine 11°32′ E 50°04′ N 32 3.3 3.84 0.38 155 916 Weisser Main I Rhine 11°33′ E 50°04′ N 42 4.5 4.96 0.59 67 3817 Aufsess I Rhine 11°15′ E 49°50′ N 54 5.9 6.17 0.55 147 2318 Weissmain I Rhine 11°13′ E 50°02′ N 70 2.6 2.53 0.23 15 619 Itz I Rhine 11°02′ E 50°20′ N 34 4.2 4.92 0.50 31 1020 Mistelbach I Rhine 11°31′ E 49°55′ N 32 2.1 2.35 0.16 8 523 Veurs III Maas 5°49′ E 50°45′ N 38 1.9 2.01 0.18 12 124 Aisne III Maas 5°32′ E 50°23′ N 122 8.9 7.23 0.50 55 3025 Lesse III Maas 5°10′ E 49°60′ N 120 11.2 8.84 0.55 60 2026 Solre III Maas 4°05′ E 50°12′ N 80 6.4 5.98 0.53 1627 Witte Nete IV Scheldt 5°06′ E 51°14′ N 79.2 2.3 2.21 0.37 10 1029 Steenputbeek I Scheldt 4°17′ E 50°43′ N 80 2.6 2.49 0.30 2 130 Verrebeek IV Scheldt 3°44′ E 50°48′ N 38.2 1.3 1.56 0.08 3 131 Maarkebeek IV Scheldt 3°41′ E 50°48′ N 36 1.3 1.34 0.10 12 132 Piétrebais IV Scheldt 4°44′ E 50°45′ N 75.6 4.9 4.68 0.62 3 333 Trouille IV Scheldt 4°00′ E 50°24′ N 76 3.6 3.46 0.45 1534 St-Georges IV Scheldt 3°31′ E 50°15′ N 80 4.5 4.24 0.49 2235 Aa IV Scheldt 2°03′ E 50°34′ N 75.8 3.1 2.98 0.43 1336 Rye IV N. Engl/Wales 1°03′ W 54°14′ N 74 9.5 8.85 0.56 172 4138 Hodder IV N. Engl/Wales 2°31′ W 53°55′ N 74 7.3 6.92 0.55 6839 Alwen IV N. Engl/Wales 3°27′ W 53°00′ N 66 3.1 3.04 0.34 340 Terrig IV N. Engl/Wales 3°07′ W 53°07′ N 74 3.9 3.75 0.25 341 Dulas IV N. Engl/Wales 4°04′ W 52°07′ N 66 4.9 4.82 0.51 1042 Cennen IV N. Engl/Wales 3°59′ W 51°52′ N 78 6.9 6.45 0.55 1643 Neet IV S. England 4°30′ W 50°47′ N 50 5 4.47 0.37 21 444 Tamar IV S. England 4°23′ W 50°44′ N 54 4.7 4.90 0.41 1945 Caerhayes IV S. England 4°50′ W 50°17′ N 100 1.6 1.53 0.17 12 246 Brandy Stream IV S. England 1°19′ W 51°02′ N 47.8 8.8 9.65 0.72 2547 Settrington Beck IV N. Engl/Wales 0°43′ W 54°07′ N 66 2.5 2.45 0.25 5 148 Pocklington Beck IV N. Engl/Wales 0°46′ W 53°56′ N 58 3.1 3.12 0.36 6 349 Menathorpe Beck IV N. Engl/Wales 0°49′ W 54°05′ N 58 6.9 7.20 0.47 7 7

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therefore less likely to evolve under a stepwise mutationmodel (SSM). Hence, these three loci were excluded fromall analyses based on the assumption of a SMM.

Allele frequencies at each locus and for each populationwere tested for conformity to Hardy–Weinberg equilib-rium and for gametic disequilibrium among all combina-tions of loci using genetix (Belkhir et al. 2000) Geneticvariability of populations was estimated by the observedaverage number of alleles per locus (ko), the expectednumber of alleles at the mean sample size of 60 genes (k60;Ewens 1972) and expected heterozygosity (He; unbiasedestimate; Nei 1978).

Recent population bottlenecks were inferred by usingthe approach of Cornuet & Luikart (1996) and the programbottleneck (Piry et al. 1999). As recommended when ana-lysing a small number of microsatellite loci, the Wilcoxonrank sign test was used to test the significance of hetero-zygote excess under three different models of microsatelliteevolution, the infinite allele model (IAM), the SMM anda two-phase model with 20% large step mutations (TPM).To examine additional evidence for recent populationbottlenecks, the distribution of allele frequency classeswas tested for a distortion from the normal ‘L-shaped distri-bution’ (Luikart et al. 1998) using bottleneck.

The impact of ancient population fragmentation andbottlenecks during the Pleistocene history of populationson their genetic variability was investigated by comparinggenetic variability among historically separated popula-tions. In such analyses contemporary population size mustbe considered, since theory predicts that the combined

impact of genetic drift and mutation generates a positivecorrelation between effective population size and geneticvariability (Crow & Kimura 1970; Frankham 1996). Thus,as a first step, the correlation between two surrogates ofeffective population size (patch size and upstream size asdescribed above) and genetic variability was analysed.

Subsequently we used two approaches to estimate thecontribution of historic factors to the variance in geneticvariability. First, we tested for differences in genetic vari-ability among groups of populations on two hierarchicallevels; haplotype lineages and geographical groups withinlineages (as described above) using a covariance approach(ancova). Phylogenetic groups formed the independentvariables (factors), genetic variability the dependent vari-able, and upstream size the covariant. Since these groupsof populations were separated by events as old as 1–2million years (Englbrecht et al. 2000), differences ingenetic variability among them reflect ancient histor-ical events. Second, the impact of postglacial populationexpansions were analysed using a correlation between lati-tudinal and longitudinal position of populations withinhaplotype lineages and genetic variability in a multipleregression approach. Genetic variability was used as thedependent variable, and upstream size, latitudinal andlongitudinal position as independent variables. A rapidpostglacial population expansion would be detected as anegative correlation towards the direction of the expansion(Ibrahim et al. 1996; Bernatchez & Wilson 1998).

The significance of pairwise and overall differentiationamong populations was estimated by using permutation

Fig. 1 Geographic distribution of samplesites (symbols) and mtDNA haplotypelineages (roman numbers). The dottedlines represent the range of haplotypelineages (Englbrecht et al. 2000; Volckaertet al. 2002). Different colours are used forsample sites from different haplotypelineages (I = white; group III = grey, groupIV = black). Different symbols are used forsample sites from different geographicalgroups (� = Danube, � = Elbe, � =Rhine(Main), � = Sweden, � = Scheldt,� = southern England, ∆ = northernEngland/Wales). Note that dots mayrepresent a group of sample sites. Theexact geographical locations of all samplesites can be found in Table 1.

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tests of conventional F-statistics according to Weir &Cockerham (1984). The magnitude of the pairwise diverg-ence among populations was estimated by two complement-ing approaches. First we calculated pairwise Cavalli-Sforza& Edward (1967) chord distances (DCE) using genetix,which does not assume constant population sizes and con-stant mutation rates across loci. Second, we calculated aδµ2 analogue, which assumes a stepwise mutation model(SSM) and takes allele size differences into account. In orderto avoid a bias of δµ2 as defined by Goldstein et al. (1995)by loci with large allele size differences, allele sizes of lociwere first standardized according to Goodman (1997)before an analogue of δµ2 was calculated from these stand-ardized values using the program rstcalc (Goodman1997). Simulation and empirical studies have shown thatsuch a combination of evidence based on both models(SMM and nonSMM) is a powerful approach to inferphylogenetic relationships among populations (Takezaki& Nei 1996; Angers & Bernatchez 1998). Whereas DCEhas proved to reveal a reliable topology of populationtrees, but unreliable branch length, the distribution of δµ2

exhibits a better fit to linearity with time, but consistentlyfails to produce a reliable tree topology (Takezaki & Nei 1996).The phylip software package (Felsenstein 1993) was usedin order to investigate the phylogenetic relationship of popu-lations. First, a neighbour-joining (NJ) tree was constructedfrom the DCE matrix. Second the approach of (Angers &Bernatchez 1998) to construct a hybrid tree from both dis-tance matrices (DCE and standardized δµ2) was adopted.The usertree option of fitch from phylip was used toenforcing the topology of the DCE tree, while extractingbranch length from the standardized δµ2 matrix using theminimum evolution criterion (Kidd & Sgaramella-Zonta1971; Rzhetsky & Nei 1993). The stability of nodes of bothtrees was tested by bootstrapping (1000 iterations).

Correlation between geographical distances and geneticdistances were investigated using Mantel tests (ntsys-pc;Rohlf 1998).

Results

Genetic variability within populations

All 10 microsatellite loci showed considerable variability,with the number of alleles ranging from eight to 97 allelesper locus across all samples. A genotypic table of all lociand populations is provided as an electronic appendix(supplementary material). Distribution and frequencies ofalleles across sites allowed for 311 tests for deviation fromHardy–Weinberg equilibrium. Only three proved to be signi-ficant, which is not more than expected by chance. None ofthe combined tests (either combining across loci or across sites)was significant. Genetic variability varied considerably amongpopulations; for example k60 ranged from 1.3 to 9.8 alleles/locus, and expected heterozygosity ranged from 0.10 to 0.68(Table 1). Such marked differences in genetic variability ofpopulations were analysed using the following steps.

Single regression analyses across all populations showedthat patch size was correlated positively with k60 (r2 = 0.38,P = 0.000) and He (r2 = 0.24, P = 0.004). Upstream size showedan even stronger correlation to k60 (r2 = 0.58, P = 0.000) andHe (r2 = 0.66, P = 0.000), compared to patch size. In a mul-tiple regression approach (data not shown) using both patchsize and upstream size together with sample size as inde-pendent variables, only upstream size was significantlycorrelated to k60 and He. Upstream size rather than patch sizewas thereby used as a surrogate for effective populationsize in further analyses since it was available for morepopulations.

A covariance approach using upstream size as covariateshowed that there were no significant differences in hetero-zygosity between the haplotype lineages and betweengeographical groups within lineages (Table 2). Allelic rich-ness was also not significantly different among haplotypelineages and between geographical groups within haplotypelineage IV. In contrast, there were significant differencesamong allelic richness of the geographical groups within

d.f. k60 He

Among haplotype lineagesF of regression 1,38 0.79 (0.000) 0.79 (0.000)F between haplotype groups 2,38 2.0 (0.295) 0.1 (0.950)F interaction 2,36 0.2 (0.834) 0.1 (0.904)

Within haplotype lineage IR of regression 1,13 0.86 (0.000) 0.77 (0.000)F between geographical groups 2,14 8.1 (0.005) 2.6 (0.109)F interaction 2,12 1.0 (0.380) 5.5 (0.021)

Within haplotype lineage IVR of regression 1,16 0.76 (0.000) 0.78 (0.000)F between geographical groups 2,16 3.1 (0.070) 0.1 (0.894)F interaction 2,14 2.9 (0.091) 0.8 (0.488)

Table 2 Analyses of covariance testing theinfluence of the factor ‘haplotype lineage’and the factor ‘geographical group withinhaplotype lineage’ on the multiple regres-sion between and genetic variability (k60and He) vs. upstream size and sample size(F-values) of European bullhead popula-tions. The first row tests the significance ofthe multiple regression (within-groupslope). The second row tests for differencesbetween the groups and the third row teststhe interaction, i.e. for differences in slopeof each regression. Error probabilities aregiven in brackets (significant results atP < 0.05 are in bold)

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haplotype lineage I: the Danube populations showed thehighest levels of variability followed by the Elbe popula-tions. The Rhine populations showed the lowest levels ofvariability within haplotype group I (Table 2, Fig. 2a).

A multiple regression using latitudinal position andlongitudinal position together with upstream size as inde-pendent variables showed that these multiple regressionmodels explained a large proportion of the genetic varianceamong populations in all cases (multiple R = 0.78–0.94,Table 3). Within both haplotype lineages only upstream sizehad a significant impact on He. However, there was asignificant, negative trend in the correlation between k60and latitude and longitude within haplotype group I, indi-cating a reduced genetic variability in more northern andwestern populations (Table 3). In contrast, latitude andlongitude had no significant impact on genetic variabilityof populations in haplotype lineage IV (Table 3).

A test for population bottlenecks revealed some evidencein 12 populations. Ten populations had significant hetero-zygote excesses according to the IAM, five populations

according to the TPM, two populations according to theSMM and five populations exhibited a shift in the distribu-tion of allele frequency classes (Table 4, Fig. 3b).

Population subdivision

There was a high degree of differentiation among popula-tions (overall FST = 0.49). Pairwise FST values ranged from0.04 to 0.87, and permutation tests showed that all pairwisevalues were highly significant (in all cases the 999 per-muted values were smaller than the real values). The patternof marked differentiation is confirmed in more detail bythe NJ tree of DCE matrix (Fig. 3a) and the hybrid treeconstructed from both DCE and standardized δµ2 (Fig. 3b).In most cases, populations from the same drainage orgeographical areas cluster together within the NJ tree andare supported by relatively high bootstrap results (> 70%).The separation of the three haplotype lineages is alsoapparent in the NJ tree. The branch lengths separatingthese ancient splits are, however, relatively short, andbootstrapping support generally low, indicating a poorerresolution of microsatellite data for older divergence timescompared to mtDNA. Within haplotype lineage I, the Elbeand Main drainages form two clusters of closely associatedpopulations whereas the Danube populations are separatedby relatively large distances. Furthermore the Swedishpopulation group with the Elbe populations, which issupported by a high bootstrapping value (71%). The branchlengths separating the three drainage groups withinhaplotype lineage I are much larger within the hybrid tree.Among populations belonging to lineage IV, there is onlyone well-defined group comprising the populationsfrom Northern England and Wales. The remainingpopulations form an unresolved assemblage of deep-branching lineages.

Upstream size (lg)

Upstream size (lg)

Fig. 2 Correlation between allelic richness (k60) of bullhead popu-lations and upstream size among geographical groups within(a) haplotype lineage I and (b) haplotype lineage IV. bullheadpopulations. Populations from geographical groups are indicatedwith different symbols (see Fig. 1). The regression lines withingeographical groups are also shown. Results of the covarianceanalysis are listed in Table 2.

Table 3 Multiple regression analysis of genetic variability vs.upstream size and latitude and longitude. Results are reportedas beta-weights (standardized regression coefficients). Errorprobabilities are given in brackets (significant results P < 0.05 inbold; two-tailed tests)

d.f. k60 He

Haplotype lineage I 3,14Upstream size 0.77 (0.000) 0.72 (0.000)

Latitude –0.31 (0.011) –0.21 (0.193)Longitude –0.29 (0.015) –0.28 (0.084)Multiple R 0.92 (0.000) 0.83 (0.001)

Haplotype lineage IV 3,16Upstream size 0.70 (0.000) 0.78 (0.000)

Latitude 0.24 (0.146) 0.07 (0.675)Longitude 0.14 (0.404) –0.05 (0.768)Multiple R 0.80 (0.001) 0.79 (0.001)

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Pairwise DCE values ranged from 0.043 to 0.146 withinhaplotype lineage I, and from 0.016 to 0.164 within haplo-type lineage IV. The average DCE within geographicalgroups are shown in Fig. 4. Among the groups of haplo-type lineage I, DCE was significantly higher within theDanube group compared to the Elbe and Rhine groups(anova, P = 0.000). Furthermore, in the plot of genetic andgeographical distances, most Elbe and Rhine populations arebelow the regression line indicating that the lower geneticdistances in these groups are independent of geographicalisolation (Fig. 4a). Among the groups of haplotype lineageIV, DCE was significantly lower within the NorthernEngland/Wales group compared to the Southern Englandand Scheldt groups (anova, P = 0.001). Furthermore, in theplot of genetic and geographical distances, most North-ern England–Wales populations are similarly below theregression line, indicating divergence that is independentof geographical isolation (Fig. 4b).

Discussion

Genetic variation within bullhead populations

In a previous study we showed that allozyme variation inC. gobio could be explained largely by geographical range asdescribed by patch size (Hänfling & Brandl 1998b). It wasthus concluded that patch size is an appropriate surrogatefor effective population size. The current data support apositive correlation between patch size and microsatellitevariability, although correlations were not as strong as

reported previously for allozymes (e.g. He, allozymes vs.patch size, R2 = 0.89; He, microsatellites vs. patch size,R2 = 0.39). The allozyme study was, however, based on fewerpopulations (12), and most sample sites were close to thedownstream margin of the population, in contrast to themany upstream sites reported here. Thus, in the allozymestudy, patch size was almost identical to upstream size,whereas isolation of upland sites and limited gene flowwithin a population might confound a positive correlationbetween patch size and genetic variability in the micro-satellite data. Indeed, a large share of variance in micro-satellite variability among populations can be better explainedby upstream size than by patch size, indicating the formerto be a better measure of effective population size. Giventhat densities of bullheads in downstream sites are ingeneral lower or similar to upstream sites (unpublisheddata), such a finding suggests strongly that gene flowwithin bullhead populations is mainly unidirectional anddownstream, similar to Trinidadian guppies (Shaw et al.1994). The close relationship between effective populationsize and genetic variability also suggests that bullheadpopulations do not frequently fluctuate in size. Upstreampopulations of bullheads appear to be relatively stable intime and depend mainly on habitat quality and size.

Differences in genetic variation in neutral genetic markerssuch as microsatellites might arise not only from geneticdrift and mutation, but also by historical events. For example,genetic variability might be affected by population frag-mentation and bottlenecks that are often associated withrange shifts and climatic fluctuations in the Pleistocene.

Pop. no. IAM TPM SMM L-shape Pop. no. IAM TPM SMM L-shape

1 (D) 0.367 0.719 0.980 x 26 (M) 0.010 0.680 0.945 x2 (D) 0.002 0.016 0.406 x 27 (S) 0.002 0.002 0.016 no3 (D) 0.461 0.813 0.973 x 29 (S) 0.156 0.473 0.594 x4 (D) 0.500 0.752 0.945 x 30 (S) 0.094 0.094 0.156 no5 (D) 0.246 0.916 0.813 x 31 (S) 0.125 0.125 0.125 no6 (D) 0.230 0.578 0.961 x 32 (S) 0.000 0.012 0.973 x7 (D) 0.082 0.500 0.977 x 33 (S) 0.003 0.082 0.344 x8 (D) 0.014 0.473 0.953 x 34 (S) 0.064 0.064 0.766 x9 (E) 0.027 0.149 0.500 no 35 (S) 0.004 0.014 0.047 x10 (E) 0.010 0.248 0.578 x 36 (NE) 0.326 0.981 1.000 x11 (E) 0.213 0.820 0.980 x 38 (NE) 0.125 0.629 0.977 x13 (E) 0.191 0.473 0.719 x 39 (NE) 0.156 0.578 0.922 x14 (E) 0.273 0.528 0.953 x 40 (NE) 0.990 0.999 1.000 x15 (R) 0.656 0.812 0.969 x 41 (NE) 0.064 0.545 0.977 x16 (R) 0.001 0.003 0.078 no 42 (NE) 0.180 0.752 0.984 x17 (R) 0.156 0.727 0.688 x 43 (SE) 0.234 0.812 0.922 x18 (R) 0.766 0.988 1.000 x 44 (SE) 0.273 0.680 0.945 x19 (R) 0.125 0.371 0.313 x 45 (SE) 0.063 0.094 0.094 x20 (R) 0.922 0.945 0.375 x 46 (SE) 0.112 0.500 0.996 x23 (M) 0.656 0.922 1.000 x 47 (NE) 0.473 0.844 0.992 x24 (M) 0.674 0.990 0.996 x 48 (NE) 0.234 0.289 0.406 x25 (M) 0.188 0.920 0.988 x 49 (NE) 0.752 0.995 1.000 x

Table 4 Results of the bottleneck analysis.Significance of heterozygote excess accord-ing to the Wilxocon rank sign test underthe IAM, TPM (20% large step mutations)and SMM for each population (Cornuet &Luikart 1996). Shown is also the deviationof allele frequency classes from a normal L-shaped distribution (Luikart et al. 1998).Significant P-values are indicated bold.Population numbers according to Table 1,geographical groups in brackets (D =Danube, E = Elbe, R = Rhine, M = Maas, S= Scheldt, NE = Northern England/Wales,SE = Southern England)

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Fig. 3 Differentiation among European bull-head populations; (a) Neighbour joining treebased on Cavalli-Sforza & Edwards chorddistances from 10 microsatellite loci (b)Hybrid tree enforcing the topology of(a) and extracting the branch length from amatrix of standarized δµ2 distances fromseven microsatellite loci. Populations ofdistinct haplotype lineages and geographicalgroups are indicated in different coloursand symbols (see Fig. 1). In (b) significantlybottlenecked populations are indicated withan asterisk, small populations (upstream 1–2 km) are in italics.

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The bullhead is ideally suited to test such effects as detailedinformation about its phylogeography during the Pleis-tocene is available independently from mtDNA sequencesand allozymes (Hänfling & Brandl 1998b). Current data showthat two measures of microsatellite variability responddifferently to historical processes. Whereas populationheterozygosity shows no traces of historic events, our analy-sis indicates that allelic richness, independent of popula-tion size, is significantly different among drainages withinhaplotype group I. Separation times among these drain-ages were estimated at around 0.1–0.6 bp (Hänfling &Brandl 1998b), and thus allelic richness shows clear tracesof colonization events predating the last major glaciation.The absence of such a correlation within haplotype lineageIV is however, inclusive, because seven of eight populationsof the river Scheldt show some evidence of recent bottlenecks

or/and are rather small (Fig. 3b). Additionally, demo-graphic information indicates that many streams in the regionhave suffered from habitat modification and eutrophicationduring the twentieth century (Vandelannoote et al. 1998).Thus, the low levels of genetic variability found in theScheldt drainage might reflect a recent decrease in popula-tion size, which obscure ancient events. Despite theoreticalpredictions on the close correlation between heterozygosityand allelic richness, the processes associated with postgla-cial colonization can create diverging trends between bothmeasures. For example, the loss of rare alleles throughbottleneck effects or genetic drift impact more on allelicrichness, whereas rapid population expansion is predictedto increase mainly heterozygosity (Wade & McCauley 1988).The persistence of such a pattern in current populationshas recently been shown for allozyme variation in theEuropean beech and was attributed to the low mutationrate of allozymes (Fagus sylvatica L., Comps et al. 2001). Ourdata, however, demonstrate that allelic richness showsclear traces of historical events even in a marker with amarkedly higher mutation rate (Jarne & Lagoda 1996).

Colonization history

The pattern of large-scale geographical differentiation inmicrosatellites is generally congruent with that revealedby mtDNA (Englbrecht et al. 2000). In addition, themicrosatellite data revealed new insights into recentprocesses by analysing genetic structure within lineagesdefined by mtDNA data.

First, the application of nonSSM-based methods showedgenetically clearly distinct geographical groups withineach haplotype lineage. Second, relative age of separationbetween these groups could be estimated using δµ2, anSMM-based estimator of shows a generally good linearitywith time on an intraspecific level (Takezaki & Nei 1996).Subsequently we used the separation between groups thatmust have clearly originated from a postglacial coloniza-tion as a calibration to approximate the timing of theevents. Third, an analysis of allelic richness was used to testpredictions from postglacial colonization scenarios.

Within haplotype lineage I, the microsatellite data reveala clear distinction between populations from drainages ofthe Lower and Upper Danube, Elbe and Rhine (Main). Theclose genetic relationship between the Swedish populationand the Elbe populations suggests that a postglacial colon-ization of Southern Scandinavia is most likely from theriver Elbe. Such dispersal would have been possible viathe Baltic ice lake following the retreat of the ice around12 000 years ago (Eronen 1983). Danube populationsdisplay a higher level of variability than their counterpartsfrom the Elbe and Main, and differentiation among Danubepopulations is, independently of geographical distance,higher than between Elbe and Main populations. Such a

Geographic distance (lg km)1 2 3

Gen

etic

dis

tanc

e (D

CE)

0.00

0.04

0.08

0.12

0.16

Group means

N-E

ngl.

S-E

ngl.

Sche

ldt

Am

ong

grou

ps

r = 0.65, P = 0.002(b)

Geographic distance (lg km)1 2 3

Gen

etic

dis

tanc

e (D

CE)

0.04

0.08

0.12

0.16

Group means

Dan

ube

Elb

eR

hine

Am

ong

grou

ps

r = 0.62, P = 0.002(a)

Fig. 4 Isolation by distance within haplotype lineage I (a) andhaplotype lineage IV (b). On the right side of each graph a scatter-plot of pairwise genetic distances against pairwise geographicaldistances and the linear regression is shown. Significance of theregression was tested using a Mantel test (1000 permutations, onetailed test). Different symbols are used for comparisons withinthe geographical units (see Fig. 1) as well as for comparisonamong these groups (+). On the left side of each graph the means(± standard deviation) of the within and among groupcomparisons are shown.

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pattern indicates clearly that the Elbe and Main werecolonized from the Danube, in accordance with biogeograph-ical studies (Banarescu 1991) suggesting that the Danubeacted as a source of colonization of the freshwater fauna ofthe North Sea. Under the assumption that δµ2 distancesshow a good linearity with time at an intraspecific level(Takezaki & Nei 1996), the timing of this colonization eventfrom the hybrid tree can be estimated. The differentiationbetween the drainage systems of Elbe, Rhine and Danubeis several-fold higher than the differentiation of typicalpostglacial distances such those among Elbe and Swedenand among the Northern Engeland/Wales group of line-age IV (Fig. 3b). The separation between the Danube andMain/Elbe populations groups must therefore predate thelast glacial maximum. These results therefore indicatethat bullheads maintained their population during the lastglacial maximum (Britain: Devesian, Central Europe:Weichselian) in these drainages. Since survival during theprevious and most severe glaciation (Britain: Wolstonian,Central Europe: Saalian) appears unlikely, bullheads wereprobably able to colonize the Main and Elbe drainages afterthe retreat of these ice shields (Marine Oxygen IsotopeStage 5e at c. 126–115 ka bp). Palaeogeographical data areinconclusive in this respect because various temporaryconnections between the Rhine and Danube and the Elbeand Danube have existed from the middle Pleistocene tothe end of the LGM, giving the potential for colonizationduring all interglacials (Hantke 1993).

The pattern of differentiation among populationsbelonging to haplotype group IV (Scheldt and UK) is char-acterized both by relatively large genetic distances withinthe drainage system of the river Scheldt and within South-ern England, and a closely related group of populationsfrom Northern England and Wales. Isolation by distanceanalysis (Fig. 4) has furthermore demonstrated that thelower genetic differentiation within the Northern England/Wales group is not generated by the closer geographicalproximity of populations within this group. All popula-tions from the Northern England/Wales group are fromsites covered by ice during the last glaciation (Frenzel et al.1992; Jones & Keen 1993), confirming a postglacial origin.Again, accepting a postglacial origin of the Northernpopulations, and a more or less constant rate of divergenceamong populations with increasing time since separation,the hybrid tree (Fig. 3b) suggests that differentiation amongthe remaining UK populations and some of the Scheldtpopulations must predate the last glaciation. The closeproximity of population 46 (Brandy Beck, Hampshire) tothe Northern England/Wales group in particular in thehybrid tree indicates a possible postglacial colonizationof Northern England and Wales from Southern England.However, in contrast to haplotype lineage I, the geographicaldistribution of allelic richness was not consistent with asimple south–north colonization scenario, as allelic richness

was not significantly lower in populations of postglacialorigin, nor was there a general trend of decline in geneticdiversity at higher latitudes. However, a cautionary note isnecessary when interpreting the observed pattern in line-age IV. Recent bottleneck events and small current popula-tion sizes which are evident in many Scheldt populationsviolate the assumption of a clock-like divergence amongpopulations, and may have obscured older patterns in thegenetic variability of these populations. No bottleneck effectwas, however, evident for the populations from SouthernEngland. Although one population (45) originates from asmall isolated drainage (Caerhayes, Cornwall), the otherSouthern England populations are large, and we haveno reason to assume that historic population sizes wereparticularly small in Southern England. Thus it can beassumed that at least the investigated populations inSouthern Britain represent several distinct lineages ofPleistocene origin according to two possible scenarios. First,postglacial colonization of Britain could have occurredfrom several already divergent source populations, or sec-ond, populations of C. gobio could have survived the lastglaciation in Southern Britain. The first possibility seemsprobable only under the assumption that the putative sourcepopulation for the colonization of the Southern Englishdrainages (43–46) is now extinct, as none of the existingcontinental populations are related closely to them (oursampling range comprises most of the continental popu-lations of group IV). It is more likely that the range ofbullhead during the last glaciation extended as far Northas Southern England or the North Sea plains, as sup-ported further by palaeogeographical evidence (Gibbard1988). During the end of the last glaciation the riversThames, Scheldt, Maas and Rhine were connected, and weredraining northwards into a common European ‘superriver’, giving the opportunity for all haplotypes of lineages I(Rhine), III (Maas) and IV (Scheldt) to invade Britain. How-ever, only haplotypes of lineage IV, the group of haplotypesalso found in the Scheldt were found in Britain. In contrast,an opportunity of a colonization of haplotypes exclusivelyfrom lineage IV haplotypes might have arisen duringearlier glacial cycles, when the rivers Thames, the south-western British drainages together with the river Scheldtdrained west into the English Channel (Gibbard 1988).mtDNA data also support the hypothesis that the popula-tion of the Southern England represent old lineages, as twodivergent haplotypes were found exclusively in Cornishpopulations, and a nested clade analysis showed a signi-ficant past fragmentation among Scheldt and the UnitedKingdom (Volckaert et al. 2002).

Are northern glacial refugia plausible?

There is an increasing awareness that the concept of thesouthern European Peninsulas as the main Pleistocene

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refugia is not appropriate for many species (Stewart &Lister 2001). The possibility of non-Mediteranean refugiafor temperate species has been discounted mainly becauseit is generally believed that the habitat in unglaciated areasof Central and Northern Europe was dominated by per-mafrost and subzero temperatures. Nevertheless, thereis evidence that local microclimates varied considerably,and isolated pockets of shrub and tree vegetation couldprevail in areas as far north as Southern England or theArdennes (Soffer 1990). Furthermore, palaeontologicaldata suggest that additional cryptic northern refugia mighthave existed throughout Central and Northern Europe evenfor temperate species, let alone cold-adapted species suchas C. gobio (Stewart & Lister 2001). First genetic evidence forsuch a hypothesis came from (Bilton et al. 1998) who showedthe existence of Central European refugia for three shrewspecies. Phylogeographical studies of other Europeanfreshwater such as chub, perch and barbel also suggest theexistence of at least one western European refugia north ofthe Pyrennes during the LGM (Durand et al. 1999; Nesboet al. 1999; Kotlik & Berrebi 2001). However, it remainsunclear whether the relative homogeneity within thewestern group reflects the existence of a single refugium,or in fact the limited power of mtDNA to disclose morerecent processes. The combined evidence of mtDNA studies(Englbrecht et al. 2000; Volckaert et al. 2002) and the presentmicrosatellite data do, however, suggest strongly thatglacial refugia during the last glacial maximum, at least forcold-adapted species like C. gobio, existed as far North asCentral Germany (Main, Elbe) and Southern England, inparticular Cornwall. Such a proposal is consistent with theidea of (Thienemann 1950) who classified bullhead asa ‘Southern ice cap marginal species’; a species whichexperiences population expansions during cold times,and retreats during interglacials to mountain refugia. Therange of such a species would be expected to extendclose to the glacial margin. Unfortunately, the fossilevidence provides little opportunity to test alternativehypothesis about the Pleistocene history of Europeanfreshwater fishes. Fish-remains are considerably under-studied in the European Quaternary (expect with respectto Holocene archaeological sites), and neither of the sparsereviews available (Stuart 1974; Stinton 1985) provides anyfossil records for C. gobio in the United Kingdom orGermany.

There are, however, at least three arguments that sup-port further the hypotheses of glacial refugia for C. gobiowithin Britain or Central Germany. First, harsh environ-mental conditions during glacial maxima including per-mafrost have also been suggested for the European NorthSea coast, and thus for the whole range of populations fromhaplotype group IV (Fig. 1). Second, the bullhead is welladapted to withstand such conditions. Breeding studies(personal observation) demonstrate that temperatures of

6 °C are sufficient for breeding and larval development,and normal feeding behaviour is observed at temperaturesjust above freezing (Elliott & Elliott 1995). Third, the pres-ence of refugial species in the southwest of England is notunknown, and a small number of ‘Lusitanian’ taxa areknown here, such as the heath, Erica ciliaris and the gastro-pod, Theba pisana (Vincent 1990).

Conclusion

In summary, microsatellites allow a more informativeinterpretation of the genetic structure within distinctlineages of populations identified previously by mtDNAanalysis. Although genetic variability in C. gobio can beexplained largely by current population size, there is clearevidence that historical events have left their imprint onallelic diversity of populations. Population differentiationin microsatellite markers as estimated by a combination ofSMM and non-SMM based methods and analysis of geneticvariability within populations suggest strongly that bull-head populations persisted most probably throughout thelast major glaciation within the British Isles and the riversElbe and Main. To our knowledge, these results provide thefirst indication of such refugial persistence of a freshwaterfish in such close proximity to the glacial margins. Suchobservations, if supported elsewhere, challenge the widelyaccepted view that the primary freshwater fauna of theBritish Isles consists exclusively of postglacial colonists(Wheeler 1977). Moreover, the study demonstrates theeffectiveness of utilizing independent molecular markersystems to examine historical processes, especially aseach may detect processes on different evolutionarytime scales.

Acknowledgements

This project has been funded by an EU Marie-Curie fellowshipto B. Hänfling and by the Ministry of the Flemish Community(AMINAL/TWOL and VLINA/97/01). J. Holcik, R. Klupp,P. Seeuws, H. Verreycken, P. Gérard, Th. De Mol, J.C. Philippart, M.Holl collected samples. Various UK Environment Agencies providedinformation on distribution of bullheads and assisted the samp-ling in the field. Bob Trigg and the Hull International FisheriesInstitute assisted in all field collections of bullheads. We benefitedgreatly from discussions with H. Griffith.

Supplementary material

The following material is availiable from http://www.blackwell-science.com/products/journals/suppmat/MEC/MEC1563/MEC1563sm.htm

Appendix Genotypic table of 44 populations at 10 microsatelliteloci Population numbers are according to table 1

Table A1. Colmuns C-L give the length of both alleles in basepairs(including flanking sequences) fr each individual at each locus

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